train YOLOv7 on VOC

README.md

@@ -71,7 +71,7 @@ python train.py --cuda -d voc --root path/to/VOCdevkit -v yolov1 -bs 16 --max_ep
 | YOLOv3 | DarkNet-53    |  640  |  √   |  150  | 82.0 |                          |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpy/yolov3_voc.pth) |
 | YOLOv4 | CSPDarkNet-53 |  640  |  √   |  150  | 83.6 |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpy/yolov4_voc.pth) |
 | YOLOv5 | CSPDarkNet-L  |  640  |  √   |  150  |      |                          |                   |                    |  |
-| YOLOX  | CSPDarkNet-L  |  640  |  √   |  150  |      |                          |                   |                    |  |
+| YOLOX  | CSPDarkNet-L  |  640  |  √   |  150  | 84.6 |                          |                   |                    |  |
 | YOLOv7 | ELANNet       |  640  |  √   |  150  |      |                          |                   |                    |  |
 
 *All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on VOC2007 test. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.*

models/yolov7/yolov7_backbone.py

@@ -8,7 +8,7 @@ except:
     
 
 model_urls = {
-    "elannet": "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/yolov7_elannet.pth",
+    "elannet": "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/yolov7_elannet_large.pth",
 }
 
 # --------------------- CSPDarkNet-53 -----------------------